Method, device, and computer program product for data protection

ABSTRACT

Embodiments of this disclosure relate to a method, a device and a computer program product for data protection. The method comprises determining objects selected by a user in a set of objects, and automatically generating one or more corresponding filtering conditions according to the objects selected by the user. The method further comprises automatically setting a predetermined protection policy for objects meeting the filtering conditions in the set of objects. In the embodiments of this disclosure, corresponding filtering conditions are automatically generated according to some protected objects selected by a user to form a dynamic filter, without manually setting the filtering conditions by the user, thereby improving the user experience of a data protection system.

TECHNICAL FIELD

Embodiments of this disclosure generally relate to the field of datastorage technologies, and in particular, to a method, a device, and acomputer program product for data protection.

BACKGROUND

Data protection refers to the protection of data of an organization orindividual to prevent data loss due to a failure. Different dataprotection policies may be set for different types of data, for example,how many backups can be set, whether remote or cloud backup is set, etc.Data can be recovered by backup in the event of a data failure ordisaster, thus avoiding unnecessary losses.

With the development of network technologies, data protection systemsextend data from a data center to a cloud environment. A user mayconfigure information of cloud storage in a data protection system andthen select a disaster-tolerant virtual machine (VM), thus backing up tothe cloud regularly. If a production machine of the user is failed andbecomes unavailable, a virtual machine may be selected from the dataprotection system and deployed directly to the cloud until theproduction machine is recovered.

SUMMARY OF THE INVENTION

A method, a device, and a computer program product for data protectionare provided in embodiments of this disclosure.

In an aspect of this disclosure, a method for data protection isprovided. The method comprises: determining objects selected by a userin a set of objects; generating one or more filtering conditionsaccording to the objects selected by the user; and setting apredetermined protection policy for objects meeting the one or morefiltering conditions in the set of objects.

In another aspect of this disclosure, an electronic device is provided.The device comprises a processing unit and a memory, wherein the memoryis coupled to the processing unit and has instructions stored thereon.When the instructions are executed by the processing unit, the followingactions are performed: determining objects selected by a user in a setof objects; generating one or more filtering conditions according to theobjects selected by the user; and setting a predetermined protectionpolicy for objects meeting the one or more filtering conditions in theset of objects.

In yet another aspect of this disclosure, a computer program product isprovided. The computer program product is tangibly stored in anon-transitory computer-readable medium and comprisescomputer-executable instructions. When executed, the computer-executableinstructions cause a computer to perform the method or process accordingto the embodiments of this disclosure.

The summary is provided to introduce the choice of concepts in asimplified form, which will be further described in the detaileddescription below. The summary is neither intended to identify keyfeatures or major features of this disclosure, nor intended to limit thescope of each embodiment of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objectives, features, and advantages of thisdisclosure will become more apparent based on more detailed descriptionof example embodiments of this disclosure with reference to accompanyingdrawings, wherein identical reference numerals usually representidentical elements in the example embodiments of this disclosure.

FIG. 1 is a schematic diagram of an operating environment of a dynamicfilter according to an embodiment of this disclosure;

FIG. 2 is a flowchart of a method for data protection according to anembodiment of this disclosure;

FIG. 3 is a schematic diagram of extracting objects selected by a useraccording to an embodiment of this disclosure;

FIG. 4 is a schematic diagram of performing unsupervised clustering onobjects not selected by a user according to an embodiment of thisdisclosure;

FIG. 5 is a schematic diagram of a decision tree according to anembodiment of this disclosure;

FIG. 6 is a schematic diagram of judging whether a decision treesplitting stop condition is met according to an embodiment of thisdisclosure;

FIG. 7 is a schematic diagram of generating a dynamic filter accordingto an embodiment of this disclosure; and

FIG. 8 is a schematic block diagram of a device that can be used toimplement an embodiment of this disclosure.

DETAILED DESCRIPTION

Preferred embodiments of this disclosure will be described in moredetail below with reference to the accompanying drawings. Some specificembodiments of this disclosure have been shown in the accompanyingdrawings. However, it should be understood that this disclosure can beimplemented in various forms and should not be limited by theembodiments described here. In contrast, the embodiments are provided tomake this disclosure more thorough and complete, and the scope of thisdisclosure can be fully conveyed to those skilled in the art.

The term “include/comprise” and its variants used herein indicate openinclusion, i.e., “including/comprising, but not limited to.” Unlessspecifically stated, the term “or” indicates “and/or.” The term “basedon” indicates “based at least in part on.” The terms “an exampleembodiment” and “an embodiment” indicate “at least one exampleembodiment.” The term “another embodiment” indicates “at least oneadditional embodiment.” The terms “first,” “second,” and the like mayrefer to different or identical objects, unless otherwise explicitlyindicated.

Various protection policies can be set to protect various objects in adata protection system. Conventionally, a user may set a protectionpolicy manually for each object that needs protection, or the user maymanually create a filtering condition so that a protection policy isautomatically set for an object meeting the filtering condition. Afterthe user manually creates appropriate filtering conditions, similarobjects in the future can be automatically added to the correspondingprotection policies. However, manually creating filtering conditionsrequires a lot of manual operations by the user, which takes a lot oftime and affects the user experience.

Therefore, a solution of automatically generating a dynamic filter whena protection policy is created is proposed in the embodiments of thisdisclosure. Different from the conventional manner of setting filteringconditions by a user manually, in the embodiments of this disclosure,corresponding filtering conditions are automatically generated accordingto some protected objects selected by a user to form a dynamic filter,without manually setting the filtering conditions by the user, therebyimproving the user experience of data protection products. According tothe embodiments of this disclosure, the user does not need to analyzeattributes of objects to create filtering conditions from scratch, whichnot only simplifies the operation of setting filtering conditions, butalso reduces a lot of configuration time for the user.

The inventor of this application noticed that a dynamic filter can beautomatically generated based on analysis of protected objects selectedby the user, so that subsequent similar objects can be automaticallyassigned to the same protection policy. Therefore, an intelligentsolution of automatically generating a dynamic filter based on objectsselected by a user when a protection policy is created is proposed inthis disclosure. According to the embodiments of this disclosure, theuser only needs to select a part of the objects that he/she wants toprotect, which can avoid complex operations during creation of thedynamic filter.

Optionally, in some embodiments of this disclosure, an unsupervisedclustering method and a supervised classification algorithm are combinedto generate filtering conditions for target protection policies, whichimproves the accuracy of the generated filtering conditions. Inaddition, in some embodiments of this disclosure, a decision tree (suchas a classification and regression tree (CART)) is also used for fastclassification of objects, which speeds up the generation of filteringconditions.

The basic principle and several example implementations of thisdisclosure are described below with reference to FIG. 1 to FIG. 8. Itshould be appreciated that the example embodiments are given only toenable those skilled in the art to better understand and then implementthe embodiments of this disclosure, but not to limit the scope of thisdisclosure in any way.

FIG. 1 is a schematic diagram of operating environment 100 of a dynamicfilter according to an embodiment of this disclosure. As shown in FIG.1, there are a plurality of objects 110 in a data protection system. Theobjects may be assets that a user needs to protect, for example, avirtual machine (VM), a structured query language (SQL) database, a filesystem disk, and so on. Different protection policies may be requiredfor different objects in these objects 110. For example, more databackups may be required for a more important database.

Dynamic filter 120 can filter objects 110 so as to automaticallydetermine whether each object meets a filtering condition of a targetprotection policy. The “dynamic filter” includes a target protectionpolicy and one or more filtering conditions, and objects meeting thisfiltering condition or these filtering conditions will be automaticallyassigned to the target protection policy. In general, separate dynamicfilters can be set for respective target protection policies. Thedynamic filter may include one or more filtering conditions. As shown inFIG. 1, dynamic filter 120 includes three example filtering conditions125. The first filtering condition is to define “name of data center,”the second filtering condition is to define “type of operating system,”and the third filtering condition is to define “size of virtualmachine.” For each object 110, dynamic filter 120 judges whether itmeets each filtering condition, and sets target protection policy 130for the object if it meets all the filtering conditions; or does not setany target protection policy if it does not meet all the filteringconditions, as shown by 140. For example, three backups may be set in anexample of target protection policy 130, one of which is in the cloud.

When a new object is found by a data protection product, dynamic filter120 can automatically judge whether the new object meets the filteringconditions, and target protection policy 130 will be set for the objectmeeting the filtering conditions. The dynamic filter may include aplurality of filtering conditions, which may be connected by logical“AND” or “OR.” Each filtering condition may be a simple logicalstatement about an object attribute. For example, the third filteringcondition in filtering condition 125 is “size of virtual machine is lessthan 100 GB,” wherein “size of virtual machine” is an object attribute,“less than” is a logical operator, and “100 GB” is a comparison value.

In some embodiments, the object may be a virtual machine, and examplesof attributes of a virtual machine object that can be used to buildfiltering conditions are shown in Table 1 below.

TABLE 1 Attributes of a virtual machine object used to build filteringconditions Attributes of a virtual machine Type Description Name of datacenter String Name of a data center that supports the virtual machineName of data String Name of a data repository that repository providesstorage for the virtual machine Type of operating String Type of anoperating system of the system virtual machine Name of virtual StringDisplay name of the virtual machine machine Folder name of String Foldername of the virtual machine virtual machine Resource pool of String Nameof a resource pool that virtual machine provides resources of thevirtual machine Size of virtual Integer Byte size of the virtual machinemachine Label of virtual Array of Label of the virtual machine machinestrings

In addition, various logical operators are also included in filteringconditions, and logical operators for building the filtering conditionsare shown in Table 2 below.

TABLE 2 Logical operators for building filtering conditions Logicaloperator Description Begin with . . . For comparing strings End with . .. For comparing strings Contain For comparing strings Not contain Forcomparing strings Equal to For comparing strings, arrays of strings, andintegers Not equal to For comparing strings, arrays of strings, andintegers Include For checking whether an array of strings includes astring Not include For checking whether an array of strings does notinclude a string Less than For comparing integers Greater than Forcomparing integers

FIG. 2 is a flowchart of method 200 for data protection according to anembodiment of this disclosure. As shown in FIG. 2, in 202, objectsselected by a user in a set of objects are determined. For example, theuser may select objects that he/she wants to protect from a set ofobjects, and the objects selected by the user are extracted for analysisto create internal filtering conditions. The user may select all or someof the objects that he/she wants to protect, and examples of the objectsinclude, but are not limited to, a virtual machine, an SQL database, afile system disk, and so on.

In 204, one or more filtering conditions are generated according to theobjects selected by the user. For example, each condition includes anobject attribute, a logical operator, and a comparison value, and theone or more filtering conditions and a predetermined protection policymay form the dynamic filter of this disclosure. Filtering conditionscorresponding to the objects are determined by analyzing the objectsselected by the user. In some embodiments, objects not selected by theuser may be firstly clustered, and then a decision tree is generated byusing the result of clustering. In the decision tree, a path from thenode corresponding to the objects selected by the user to a root node isa filtering condition. In some embodiments, the automatically generatedfiltering conditions may not be accurate enough, so the automaticallygenerated filtering conditions may be presented to the user and thenmicro-adjustment of the filtering conditions by the user may bereceived. As such, the accuracy of the filtering conditions can beimproved.

In 206, a predetermined protection policy is set for objects meeting theone or more filtering conditions in the set of objects. In someembodiments, the objects that the user wants to protect may not becompletely selected, so the objects meeting the one or more filteringconditions may include objects not selected by the user, and the usermay be reminded whether one or more objects are missed in the selection.Alternatively, a predetermined protection policy may be directly set forall the objects meeting the one or more filtering conditions.

Therefore, according to the embodiment of this disclosure, correspondingfiltering conditions are automatically generated according to someprotected objects selected by a user to form a dynamic filter, which caneliminate the operation of manually setting filtering conditions by theuser, thereby improving the user experience of data protection products.

In some embodiments, after the dynamic filter that includes filteringconditions is generated, it is automatically determined, for a newlyfound new object, whether the new object meets the filtering conditions.If the new object meets the filtering conditions, a predeterminedprotection policy is directly set for the new object. If the new objectdoes not meet the filtering conditions, there is no need to set apredetermined protection policy for the new object. As such, automaticprotection policy management can be performed not only on the existingobjects, but also on the new object.

FIG. 3 is a schematic diagram of extracting objects selected by a useraccording to an embodiment of this disclosure. In a data protectionsystem as shown in FIG. 3, there is a set of objects 310, such asvirtual machine objects, including objects 311-325 and so on. The userselects objects 311, 316, 317, 320, and 321 from the set of objects 310as objects to be protected. Correspondingly, the set of objects 310 isdivided into two parts, i.e., part 340 selected by the user, includingobjects 311, 316, 317, 320, and 321; and part 330 not selected by theuser, including objects 312, 313, 314, 315, 318, 319, 322, 323, 324,325, and so on.

According to the embodiment of this disclosure, the user operation ofcreating a dynamic filter only includes selecting objects to beprotected, and the objects are expected to be filtered by the createddynamic filter. As shown in FIG. 3, objects 311, 316, 317, 320, and 321selected by the user are reserved as a special object group and assignedwith a reserved cluster identifier (ID), and do not participate insubsequent clustering operations.

According to the embodiment of this disclosure, the generation of adecision tree may include two stages. In the first stage, as shown inFIG. 4, all the objects are pre-classified by using an unsupervisedmachine learning technology. The result of pre-classification is tocreate a classification result table that includes cluster IDs andcorresponding object IDs. In the second stage, as shown in FIG. 5 toFIG. 7, supervised classification is performed by using the result ofpre-classification in the first stage, and the supervised classificationin the second stage may be classification based on a decision tree.Decisions made at the nodes in the decision tree will constitute adynamic filter.

First of all, the procedure proceeds to the first stage. FIG. 4 is aschematic diagram of performing unsupervised clustering on objects notselected by a user according to an embodiment of this disclosure. Asshown in FIG. 4, unsupervised clustering is performed on remaining part330 not selected by the user, including objects 312, 313, 314, 315, 318,319, 322, 323, 324, 325, and so on, thereby clustering them into sevenclasses. A cluster of cluster ID value 1 includes object 312 and so on,a cluster of cluster ID value 2 includes object 314 and so on, a clusterof cluster ID value 3 includes object 318 and so on, a cluster ofcluster ID value 4 includes objects 313, 315, and so on, a cluster ofcluster ID value 5 includes object 319 and so on, a cluster of clusterID value 6 includes objects 322, 325, and so on, and a cluster ofcluster ID value 7 includes objects 323, 324, and so on. Objects 311,316, 317, 320, and 321 selected by the user are assigned with a reservedcluster ID value 0. As shown in FIG. 4, all objects may be classifiedinto eight classes after clustering, thus forming data set 410 forgenerating a decision tree.

In some embodiments, the objects not selected by the user may beclassified into K groups by using a K-means algorithm without presettingthe number of groups, where K represents the number of user groups afterclustering. K-means is an unsupervised clustering algorithm featuredwith simpleness and high computational speed. By using the K-meansalgorithm, the objects not selected by the user can be classified intoseveral clusters according to attributes of the objects.

Next, the procedure proceeds to the second stage. FIG. 5 is a schematicdiagram of decision tree 500 according to an embodiment of thisdisclosure. Decision tree is an important algorithm type for predictingmodeling machine learning. It has a flowchart-like structure, in whicheach internal node represents a “test” on an attribute, each branchrepresents the result of a test, and each leaf node represents a classlabel (a decision made after calculation of all attributes). A path froma root node to a leaf node represents a classification rule. In decisionanalysis, decision tree and closely related influence graphs are used astools supporting visual and analytical decision-making. Decision tree isoften used in decision analysis to help determine the most likelytarget, and is also a policy for popular tools in machine learning.Decision tree is a widely used non-parametric efficient machine learningmodeling technology for regression and classification problems. To finda solution, the decision tree makes sequential and hierarchicaldecisions on outcome variables according to predictor data. A regressionor classification model is built in the form of a tree structure by thedecision tree. The data set is decomposed into increasingly smallersubsets by the decision tree, and meanwhile, the associated decisiontree is gradually developed to finally form a tree with decision nodesand leaf nodes.

A classification and regression tree (CART) is a decision treealgorithm, which is a widely used decision tree learning methodconsisting of feature selection, tree generation, and pruning, and canbe used for both classification and regression. The CART algorithmmainly consists of the following two steps: decision tree generation:generating a decision tree based on a training data set, wherein thegenerated decision tree should be as large as possible; and decisiontree pruning: pruning the generated tree by using a verification dataset and selecting an optimal sub-tree. In this case, the loss functionbeing minimum is used as a standard of pruning.

As illustrated by data set 410 shown in FIG. 4, each object has beenassigned with a cluster ID in the unsupervised clustering of the firststage. Data set 410 will be used for a classification model based on asupervised decision tree. Decision tree 500 attempts to classify theobjects into a plurality of hierarchical subsets according to clusterIDs by judging some conditions. Attributes of the objects will be usedfor condition judgment. In the CART algorithm, the most importantattribute will be selected first to classify the objects by calculatingGini values, so that the CART becomes a fast and efficient method toclassify all objects into a plurality of pre-allocated clusters.

Referring back to FIG. 5, corresponding decision tree 500 isautomatically generated through the CART algorithm according to data set410 obtained in the first stage. In the process of generating decisiontree 500, cluster IDs of the objects will be taken into account duringcalculation of a Gini coefficient in the CART algorithm, thus generatingthe shortest and most efficient path. In decision tree 500, a pathcorresponding to a cluster of the objects selected by the user is asfollows: first, the type of an operating system (OS) is judged at node510; if the type of the OS is Solaris 520, the name of a data center isjudged at node 530; if the name of the data center is DC2 540, the sizeof a virtual machine (VM) is judged at node 550; and if the size of theVM is less than 100 GB, leaf node 560 corresponding to the cluster ofthe objects selected by the user is reached. As shown in FIG. 5, inaddition to including objects 311, 316, 317, 320, and 321 selected bythe user, object set 561 at leaf node 560 also includes object 325 thatis not selected by the user, and the user may be reminded whether object325 is missed in the selection. As such, objects that the user intendsto protect can be obtained more completely according to the embodimentof this disclosure.

FIG. 6 is a schematic diagram of judging whether a decision treesplitting stop condition is met according to an embodiment of thisdisclosure. At an upper level of the decision tree, each node maycorrespond to a plurality of objects with different cluster IDs, so itmay be difficult to classify all objects into correct groups with only afew judgment conditions. With the constant splitting of the decisiontree, the objects corresponding to each leaf node will include fewerunique cluster IDs, or even some leaf nodes will include only objectswith the same cluster ID. The objects selected by the user areclassified together with other objects at the upper level of thedecision tree. With the splitting of the decision tree, the size of thenodes will be decreased, and increasingly more objects from otherclusters will be classified into other nodes. If the operation ofsplitting the decision tree causes the objects selected by the user tobe classified into a plurality of subgroups, the standard of stoppingsplitting the whole tree is met. The stop condition is that if thedecision tree is split again, the objects selected by the user will beassigned to different leaf nodes in the decision tree. That is, if thesplitting of the decision tree causes the objects selected by the userto be assigned to different leaf nodes, the splitting of the decisiontree is stopped.

As denoted by 600 in FIG. 6, object set 561 at leaf node 560 mainlyincludes objects 311, 316, 317, 320, and 321 selected by the user, andincludes only one object 325 from another cluster. On the assumptionthat leaf node 560 in the decision tree is split again, then the CARTalgorithm at node 570 will attempt to split the node into two leaf nodes580 and 590 according to VM labels, wherein leaf node 580 covers objectset 581 including objects 311 and 320, and leaf node 590 covers objectset 591 including objects 316, 317, 321, and 325. As can be seen, theobjects selected by the user will be classified into two leaf nodes, sothe decision tree splitting stop condition is met. Therefore, thesplitting in block 600 will be discarded, and the extension of decisiontree 500 will be stopped, thus completing the generation of the decisiontree.

FIG. 7 is a schematic diagram 700 of generating a dynamic filteraccording to an embodiment of this disclosure. As shown in FIG. 7, afterdecision tree 500 is generated, each filtering condition in the dynamicfilter may be generated based on a path from leaf node 560 correspondingto the objects selected by the user to root node 510. For example, thejudgment condition corresponding to leaf node 560 includes: judgmentcondition 711 in which whether the type of an OS is Solaris is judged;judgment condition 712 in which whether the name of a data center is DC2is judged; and judgment condition 713 in which whether the size of a VMis less than 100 GB is judged. Dynamic filter 710 may be furthergenerated based on the judgment conditions. Then, object set 561 may beautomatically filtered out by applying the generated dynamic filter 710to all objects 310, thus meeting the intention of the user.

Final objects filtered by the dynamic filter may be exactly the same asthe objects selected by the user, but may also include more objects. Inthis case, a prompt may be displayed on a user interface to remind theuser to check whether an object or some objects are missed in theinitial selection stage. There may be too many objects for the user toselect. In this case, the user may select only some of the objects, andthe dynamic filter can automatically help the user identify othersimilar objects. Therefore, the embodiment of this disclosure can alsohelp the user find similar objects.

FIG. 8 is a schematic block diagram of device 800 that can be configuredto implement an embodiment of this disclosure. Device 800 may be adevice or apparatus described in the embodiment of this disclosure. Asshown in FIG. 8, device 800 includes central processing unit (CPU) 801that can perform various appropriate actions and processing according tocomputer program instructions stored in read-only memory (ROM) 802 orcomputer program instructions loaded from storage unit 808 to randomaccess memory (RAM) 803. Various programs and data required for theoperation of device 800 can also be stored in RAM 803. CPU 801, ROM 802,and RAM 803 are connected to each other through bus 804. Input/output(I/O) interface 805 is also connected to bus 804.

A plurality of components in device 800 are connected to I/O interface805, including: input unit 806, such as a keyboard and a mouse; outputunit 807, such as various types of displays and speakers; storage unit808, such as a magnetic disk and an optical disc; and communication unit809, such as a network card, a modem, and a wireless communicationtransceiver. Communication unit 809 allows device 800 to exchangeinformation/data with other devices over a computer network such as theInternet and/or various telecommunication networks.

The various methods or processes described above may be performed byprocessing unit 801. For example, in some embodiments, the method can beimplemented as a computer software program that is tangibly included ina machine-readable medium, such as storage unit 808. In someembodiments, some or all of the computer program can be loaded and/orinstalled onto device 800 via ROM 802 and/or communication unit 809.When the computer program is loaded into RAM 803 and executed by CPU801, one or more of the steps or actions in the methods or processesdescribed above may be implemented.

In some embodiments, the methods and processes described above may beimplemented as a computer program product. The computer program productmay include a computer-readable storage medium having computer-readableprogram instructions for performing various aspects of this disclosureloaded thereon.

The computer-readable storage medium can be a tangible device capable ofretaining and storing instructions used by an instruction-executingdevice. For example, the computer-readable storage medium can be, but isnot limited to, an electrical storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any appropriate combination of theabove. More specific examples (a non-exhaustive list) of thecomputer-readable storage medium include: a portable computer disk, ahard disk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or flash memory), a staticrandom access memory (SRAM), a portable compact disk read-only memory(CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk,a mechanical coding device such as a punched card or a protrudingstructure within a groove on which instructions are stored, and anyappropriate combination of the above. The computer-readable storagemedium as used herein is not explained as instant signals per se, suchas radio waves or other electromagnetic waves propagated freely,electromagnetic waves propagated through waveguides or othertransmission media (e.g., light pulses propagated through fiber-opticcables), or electrical signals transmitted over wires.

The computer-readable program instructions described herein may bedownloaded from the computer-readable storage medium to variouscomputing/processing devices or downloaded to external computers orexternal storage devices over a network such as the Internet, a localarea network, a wide area network, and/or a wireless network. Thenetwork may include copper transmission cables, fiber optictransmission, wireless transmission, routers, firewalls, switches,gateway computers, and/or edge servers. A network adapter card ornetwork interface in each computing/processing device receivescomputer-readable program instructions from the network and forwards thecomputer-readable program instructions for storage in thecomputer-readable storage medium in each computing/processing device.

The computer program instructions for performing the operations of thisdisclosure may be assembly instructions, instruction set architecture(ISA) instructions, machine instructions, machine-related instructions,microcode, firmware instructions, state setting data, or source code orobject code written in any combination of one or more programminglanguages, including object-oriented programming languages as well asconventional procedural programming languages. The computer readableprogram instructions can be completely executed on a user's computer,partially executed on a user's computer, executed as a separate softwarepackage, partially executed on a user's computer and partially executedon a remote computer, or completely executed on a remote computer or aserver. In the case where a remote computer is involved, the remotecomputer can be connected to a user's computer over any kind ofnetworks, including a local area network (LAN) or a wide area network(WAN), or can be connected to an external computer (e.g., connected overthe Internet provided by an Internet service provider). In someembodiments, an electronic circuit, such as a programmable logiccircuit, a field programmable gate array (FPGA), or a programmable logicarray (PLA), can be customized by utilizing state information of thecomputer-readable program instructions. The electronic circuit canexecute the computer-readable program instructions to implement variousaspects of this disclosure.

These computer-readable program instructions can be provided to aprocessing unit of a general purpose computer, a special purposecomputer, or another programmable data processing apparatus to produce amachine, such that the instructions, when executed by the processingunit of the computer or another programmable data processing apparatus,generate an apparatus for implementing the functions/actions specifiedin one or more blocks in the flowcharts and/or block diagrams. Thesecomputer-readable program instructions may also be stored in acomputer-readable storage medium, and these instructions cause acomputer, a programmable data processing apparatus and/or another deviceto work in a specific manner, such that the computer-readable mediumstoring the instructions includes an article of manufacture thatincludes instructions for implementing various aspects of thefunctions/actions specified in one or more blocks in the flowchartsand/or block diagrams.

The computer-readable program instructions may also be loaded onto acomputer, another programmable data processing apparatus, or anotherdevice such that a series of operational steps are performed on thecomputer, another programmable data processing apparatus, or anotherdevice to produce a computer-implemented process. As such, theinstructions executed on the computer, another programmable dataprocessing apparatus, or another device implement the functions/actionsspecified in one or more blocks in the flowcharts and/or block diagrams.

The flowcharts and block diagrams in the accompanying drawingsillustrate the architecture, functions, and operations of possibleimplementations of devices, methods, and computer program productsaccording to multiple embodiments of this disclosure. In this regard,each block in the flowcharts or block diagrams can represent a module, aprogram segment, or a portion of an instruction that includes one ormore executable instructions for implementing specified logicalfunctions. In some alternative implementations, functions labeled in theblocks may occur in an order different from that labeled in theaccompanying drawing. For example, two successive blocks may actually beperformed basically in parallel, or they may be performed in an oppositeorder sometimes, depending on the functions involved. It should also benoted that each block in the block diagrams and/or flowcharts, and acombination of blocks in the block diagrams and/or flowcharts can beimplemented using a dedicated hardware-based system for executingspecified functions or actions, or can be implemented using acombination of dedicated hardware and computer instructions.

Various embodiments of this disclosure have been described above, andthe foregoing description is illustrative rather than exhaustive, and isnot limited to the disclosed embodiments. Numerous modifications andchanges are apparent to those of ordinary skill in the art withoutdeparting from the scope and spirit of the various illustratedembodiments. The selection of terms as used herein is intended to bestexplain the principles and practical applications of the variousembodiments, or the technical improvements to the technologies on themarket, or to enable other persons of ordinary skill in the art tounderstand the embodiments disclosed here.

1. A method for data protection, comprising: determining objectsselected by a user in a set of objects; generating one or more filteringconditions according to the objects selected by the user; and setting apredetermined protection policy for objects meeting the one or morefiltering conditions in the set of objects.
 2. The method of claim 1,further comprising: obtaining a new object found; determining whetherthe new object meets the one or more filtering conditions; and settingthe predetermined protection policy for the new object if it isdetermined that the new object meets the one or more filteringconditions.
 3. The method of claim 1, wherein generating one or morefiltering conditions comprises: determining objects not selected by theuser in the set of objects; performing unsupervised clustering on theobjects not selected by the user to obtain a plurality of classes andobjects corresponding to each of the classes; and generating a data setfor creating a decision tree based on the plurality of classes of theobjects not selected by the user and a reserved class of the objectsselected by the user.
 4. The method of claim 3, wherein generating oneor more filtering conditions further comprises: generating a decisiontree by supervised classification based on the data set; and generatingthe one or more filtering conditions based on a path from a leaf nodecorresponding to the reserved class to a root node in the decision tree.5. The method of claim 4, wherein generating a decision tree bysupervised classification comprises: generating the decision tree byusing a classification and regression tree (CART) algorithm; andsplitting the decision tree based on the data set until a stop conditionis met, the stop condition being that if the decision tree is splitagain, the objects selected by the user will be assigned to differentleaf nodes in the decision tree.
 6. The method of claim 1, whereinsetting a predetermined protection policy comprises: determining theobjects meeting the one or more filtering conditions in the set ofobjects; and reminding the user, based on a comparison between theobjects meeting the one or more filtering conditions and the objectsselected by the user, whether one or more objects are missed in theselection.
 7. The method of claim 1, wherein generating one or morefiltering conditions comprises: presenting the generated one or morefiltering conditions to the user; and receiving adjustment of the one ormore filtering conditions by the user.
 8. The method of claim 1, whereineach of the one or more filtering conditions comprises an objectattribute, a logical operator, and a comparison value, and a dynamicfilter is formed for the one or more filtering conditions of thepredetermined protection policy and the predetermined protection policy.9. An electronic device, comprising: a processing unit; and a memorycoupled to the processing unit and having instructions stored thereon,wherein when the instructions are executed by the processing unit, thefollowing actions are performed: determining objects selected by a userin a set of objects; generating one or more filtering conditionsaccording to the objects selected by the user; and setting apredetermined protection policy for objects meeting the one or morefiltering conditions in the set of objects.
 10. The device of claim 9,wherein the actions further comprise: obtaining a new object found;determining whether the new object meets the one or more filteringconditions; and setting the predetermined protection policy for the newobject if it is determined that the new object meets the one or morefiltering conditions.
 11. The device of claim 9, wherein generating oneor more filtering conditions comprises: determining objects not selectedby the user in the set of objects; performing unsupervised clustering onthe objects not selected by the user to obtain a plurality of classesand objects corresponding to each of the classes; and generating a dataset for creating a decision tree based on the plurality of classes ofthe objects not selected by the user and a reserved class of the objectsselected by the user.
 12. The device of claim 11, wherein generating oneor more filtering conditions further comprises: generating a decisiontree by supervised classification based on the data set; and generatingthe one or more filtering conditions based on a path from a leaf nodecorresponding to the reserved class to a root node in the decision tree.13. The device of claim 12, wherein generating a decision tree bysupervised classification comprises: generating the decision tree byusing a classification and regression tree (CART) algorithm; andsplitting the decision tree based on the data set until a stop conditionis met, the stop condition being that if the decision tree is splitagain, the objects selected by the user will be assigned to differentleaf nodes in the decision tree.
 14. The device of claim 9, whereinsetting a predetermined protection policy comprises: determining theobjects meeting the one or more filtering conditions in the set ofobjects; and reminding the user, based on a comparison between theobjects meeting the one or more filtering conditions and the objectsselected by the user, whether one or more objects are missed in theselection.
 15. The device of claim 9, wherein generating one or morefiltering conditions comprises: presenting the generated one or morefiltering conditions to the user; and receiving adjustment of the one ormore filtering conditions by the user.
 16. The device of claim 9,wherein each of the one or more filtering conditions comprises an objectattribute, a logical operator, and a comparison value, and a dynamicfilter is formed for the one or more filtering conditions of thepredetermined protection policy and the predetermined protection policy.17. A computer program product tangibly stored in a non-transitorycomputer-readable medium and comprising computer-executableinstructions, wherein when executed, the computer-executableinstructions cause a computer to perform operations, the operationscomprising: determining objects selected by a user in a set of objects;generating one or more filtering conditions according to the objectsselected by the user; and setting a predetermined protection policy forobjects meeting the one or more filtering conditions in the set ofobjects.
 18. The computer program product of claim 17, wherein theoperations further comprise: obtaining a new object found; determiningwhether the new object meets the one or more filtering conditions; andsetting the predetermined protection policy for the new object if it isdetermined that the new object meets the one or more filteringconditions.
 19. The method of computer program product 17, whereingenerating one or more filtering conditions comprises: determiningobjects not selected by the user in the set of objects; performingunsupervised clustering on the objects not selected by the user toobtain a plurality of classes and objects corresponding to each of theclasses; and generating a data set for creating a decision tree based onthe plurality of classes of the objects not selected by the user and areserved class of the objects selected by the user.
 20. The computerprogram product of claim 19, wherein generating one or more filteringconditions further comprises: generating a decision tree by supervisedclassification based on the data set; and generating the one or morefiltering conditions based on a path from a leaf node corresponding tothe reserved class to a root node in the decision tree.